The present invention relates to “smart” sensors, more particularly to algorithms for effecting adaptive learning by standard sensors.
Sensors have been referred to as “smart” if their information can be manipulated to achieve a specific outcome. According to a typical smart sensor, this manipulation of information is accomplished mainly through the use of simple rules. In principle, perhaps even one rule would suffice to attach the “smart” label to a sensor, notwithstanding the limited functionality of a single-rule brand of “smartness”; nevertheless, a smart sensor is usually characterized by plural rules. As smart sensor technology continues to develop, these rules are becoming increasingly numerous and interdependent, and are being modularized into software constructs known as “intelligent agents.” See Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Second Edition, 20 Dec. 2002.
Currently known smart sensors carry out tasks of greater complexity than in the past, but remain a function of the derived knowledge of some expert. It would be desirable for a smart sensor to be capable of deriving its knowledge directly from its measured data using data processing algorithms. The development of a robust strategy for sensor adaptation during regular operations would constitute an important advance in the evolution of sensor technology. Artificial neural networks have been considered for achieving new-generation smart sensors that are attributed with some form of adaptability. See Laurene V. Fausett, Fundamentals of Neural Networks: Architectures, Algorithms and Applications, Prentice Hall, 9 Dec. 1993; however, neural networks are significantly limited, as their models provide little physical insight into a given problem and require many examples to achieve a good understanding of the problem.